Triple

T15665426
Position Surface form Disambiguated ID Type / Status
Subject Taiwan Railways Administration E377173 entity
Predicate operatesIn P82 FINISHED
Object Nantou E125431 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Nantou | Statement: [Taiwan Railways Administration, operatesIn, Nantou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nantou
Context triple: [Taiwan Railways Administration, operatesIn, Nantou]
  • A. Nantou
    Nantou is a historic subdistrict in Shenzhen’s Nanshan District, known as the site of the old county seat and a preserved ancient town area.
  • B. Nantou County chosen
    Nantou County is a mountainous county in central Taiwan known for its indigenous communities, scenic landscapes like Sun Moon Lake, and its role in significant historical events.
  • C. Chiayi
    Chiayi is a city in southwestern Taiwan known as a gateway to the Alishan scenic area and for its role as a regional transportation and cultural hub.
  • D. Xinyi
    Xinyi is a county-level city administered by Xuzhou in Jiangsu Province, eastern China.
  • E. Xinyi
    Xinyi is a county-level city administered by Maoming in Guangdong Province, China, known for its agriculture and regional commerce.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d85cd2e28481909d4e975bee20872f completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04f0f4df08190ad2c5d78e435d8eb completed April 16, 2026, 2:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00c28f43988190aa06da8c356b9646 completed May 10, 2026, 5:38 p.m.
Created at: April 10, 2026, 4:16 a.m.